A false discovery approach for scanning spatial disease clusters with arbitrary shapes. Issue 7 (2nd July 2016)

• Record Type: Journal Article
• Authors: Li, Yanting; Shu, Lianjie; Tsung, Fugee
• Is Part Of: IIE transactions. Volume 48:Issue 7(2016)
• Page Start: 684
• Page End: 698
• Publication Date: 2016-07-02
• Subjects: Poisson distribution -- scan statistics -- false discovery rate -- simple linear step-up procedure; Industrial engineering -- Periodicals; Génie industriel; 620
• DOI: 10.1080/0740817X.2015.1133940
• Languages: English
• ISSNs: 0740-817X

Abstract

ABSTRACT: The spatial scan statistic is one of the main tools for testing the presence of clusters in a geographical region. The recently proposed Fast Subset Scan (FSS) method represents an important extension, as it is computationally efficient and enables detection of clusters with arbitrary shapes. Aimed at automatically and simultaneously detecting multiple clusters of any shapes, this article explores the False Discovery (FD) approach originated from multiple hypothesis testing. We show that the FD approach can provide a higher detection power and better identification capability than the standard scan and FSS methods, on average.